"""simple docstring"""





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
A :    int										=      logging.getLogger(__name__)
def 	snake_case__					(			):

      """simple docstring"""

      UpperCamelCase__										=			argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."     )
      parser.add_argument("--file_path"							,      type=a__							,      default="data/dump.txt"							,      help="The path to the data."     )
      parser.add_argument("--tokenizer_type"							,      type=a__							,      default="bert"							,      choices=["bert", "roberta", "gpt2"]     )
      parser.add_argument("--tokenizer_name"							,      type=a__							,      default="bert-base-uncased"							,      help="The tokenizer to use."     )
      parser.add_argument("--dump_file"							,      type=a__							,      default="data/dump"							,      help="The dump file prefix."     )
      UpperCamelCase__										=			parser.parse_args()

      logger.info(F'Loading Tokenizer ({args.tokenizer_name})'     )
      if args.tokenizer_type == "bert":
            UpperCamelCase__										=			BertTokenizer.from_pretrained(args.tokenizer_name     )
            UpperCamelCase__										=			tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            UpperCamelCase__										=			tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCamelCase__										=			RobertaTokenizer.from_pretrained(args.tokenizer_name     )
            UpperCamelCase__										=			tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            UpperCamelCase__										=			tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCamelCase__										=			GPTaTokenizer.from_pretrained(args.tokenizer_name     )
            UpperCamelCase__										=			tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            UpperCamelCase__										=			tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(F'Loading text from {args.file_path}'     )
      with open(args.file_path							,      "r"							,      encoding="utf8"     ) as fp:
            UpperCamelCase__										=			fp.readlines()

      logger.info("Start encoding"     )
      logger.info(F'{len(a__     )} examples to process.'     )

      UpperCamelCase__										=			[]
      UpperCamelCase__										=			0
      UpperCamelCase__										=			1_00_00
      UpperCamelCase__										=			time.time()
      for text in data:
            UpperCamelCase__										=			F'{bos} {text.strip()} {sep}'
            UpperCamelCase__										=			tokenizer.encode(a__							,      add_special_tokens=a__     )
            rslt.append(a__     )

            iter += 1
            if iter % interval == 0:
                  UpperCamelCase__										=			time.time()
                  logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'     )
                  UpperCamelCase__										=			time.time()
      logger.info("Finished binarization"     )
      logger.info(F'{len(a__     )} examples processed.'     )

      UpperCamelCase__										=			F'{args.dump_file}.{args.tokenizer_name}.pickle'
      UpperCamelCase__										=			tokenizer.vocab_size
      if vocab_size < (1 << 16):
            UpperCamelCase__										=			[np.uintaa(a__     ) for d in rslt]
      else:
            UpperCamelCase__										=			[np.intaa(a__     ) for d in rslt]
      random.shuffle(rslt_     )
      logger.info(F'Dump to {dp_file}'     )
      with open(a__							,      "wb"     ) as handle:
            pickle.dump(rslt_							,      a__							,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
   main()


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__snake_case				:      Optional[int]   		=			logging.getLogger(__name__)



class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None    )     -> Tuple:
									a_						:						str 							= self.layer[current_layer](_lowercase  ,      _lowercase  ,      head_mask[current_layer]    )

									a_						:						str 							= layer_outputs[0]

									return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> List[str]:
									super().__init__(_lowercase    )

									a_						:						Tuple 							= BertEncoderWithPabee(_lowercase    )

									self.init_weights()
									a_						:						int 							= 0
									a_						:						Any 							= 0
									a_						:						Tuple 							= 0

									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Tuple:
									a_						:						Dict 							= threshold





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[Any]:
									a_						:						Optional[int] 							= patience





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						str 							= 0
									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= self.inference_layers_num / self.inference_instances_num
									a_						:						Optional[int] 							= (
									    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
									    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
									)
									print(_lowercase    )





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      )     -> str:

									if input_ids is not None and inputs_embeds is not None:
												raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""    )
									elif input_ids is not None:
												a_						:						Dict 							= input_ids.size()
									elif inputs_embeds is not None:
												a_						:						Dict 							= inputs_embeds.size()[:-1]
									else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

									a_						:						Optional[Any] 							= input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
												a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
									if token_type_ids is None:
												a_						:						List[str] 							= torch.zeros(_lowercase  ,      dtype=torch.long  ,      device=_lowercase    )

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									a_						:						torch.Tensor 							= self.get_extended_attention_mask(_lowercase  ,      _lowercase  ,      _lowercase    )

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
												a_							,					a_							,					a_						:						int 							= encoder_hidden_states.size()
												a_						:						List[str] 							= (encoder_batch_size, encoder_sequence_length)
												if encoder_attention_mask is None:
															a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
												a_						:						List[Any] 							= self.invert_attention_mask(_lowercase    )
									else:
												a_						:						Optional[Any] 							= None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									a_						:						List[Any] 							= self.get_head_mask(_lowercase  ,      self.config.num_hidden_layers    )

									a_						:						List[str] 							= self.embeddings(
									    input_ids=_lowercase  ,      position_ids=_lowercase  ,      token_type_ids=_lowercase  ,      inputs_embeds=_lowercase    )
									a_						:						List[Any] 							= embedding_output

									if self.training:
												a_						:						Any 							= []
												for i in range(self.config.num_hidden_layers    ):
															a_						:						int 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						List[Any] 							= self.pooler(_lowercase    )
															a_						:						Optional[int] 							= output_layers[i](output_dropout(_lowercase    )    )
															res.append(_lowercase    )
									elif self.patience == 0:  # Use all layers for inference
												a_						:						Union[str, Any] 							= self.encoder(
												    _lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
												a_						:						Union[str, Any] 							= self.pooler(encoder_outputs[0]    )
												a_						:						List[str] 							= [output_layers[self.config.num_hidden_layers - 1](_lowercase    )]
									else:
												a_						:						Any 							= 0
												a_						:						Dict 							= None
												a_						:						Tuple 							= 0
												for i in range(self.config.num_hidden_layers    ):
															calculated_layer_num += 1
															a_						:						Optional[Any] 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						Optional[int] 							= self.pooler(_lowercase    )
															a_						:						int 							= output_layers[i](_lowercase    )
															if regression:
																		a_						:						Dict 							= logits.detach()
																		if patient_result is not None:
																					a_						:						Optional[Any] 							= patient_result.detach()
																		if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
																					patient_counter += 1
																		else:
																					a_						:						Optional[Any] 							= 0
															else:
																		a_						:						str 							= logits.detach().argmax(dim=1    )
																		if patient_result is not None:
																					a_						:						str 							= patient_result.detach().argmax(dim=1    )
																		if (patient_result is not None) and torch.all(labels.eq(_lowercase    )    ):
																					patient_counter += 1
																		else:
																					a_						:						Tuple 							= 0

															a_						:						Union[str, Any] 							= logits
															if patient_counter == self.patience:
																		break
												a_						:						str 							= [patient_result]
												self.inference_layers_num += calculated_layer_num
												self.inference_instances_num += 1

									return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> str:
									super().__init__(_lowercase    )
									a_						:						str 							= config.num_labels

									a_						:						Optional[Any] 							= BertModelWithPabee(_lowercase    )
									a_						:						int 							= nn.Dropout(config.hidden_dropout_prob    )
									a_						:						str 							= nn.ModuleList(
									    [nn.Linear(config.hidden_size  ,      self.config.num_labels    ) for _ in range(config.num_hidden_layers    )]    )

									self.init_weights()





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Optional[Any] 							= self.bert(
									    input_ids=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      position_ids=_lowercase  ,      head_mask=_lowercase  ,      inputs_embeds=_lowercase  ,      output_dropout=self.dropout  ,      output_layers=self.classifiers  ,      regression=self.num_labels == 1  ,      )

									a_						:						Optional[Any] 							= (logits[-1],)

									if labels is not None:
												a_						:						int 							= None
												a_						:						Union[str, Any] 							= 0
												for ix, logits_item in enumerate(_lowercase    ):
															if self.num_labels == 1:
																		#  We are doing regression
																		a_						:						Any 							= MSELoss()
																		a_						:						Union[str, Any] 							= loss_fct(logits_item.view(-1    )  ,      labels.view(-1    )    )
															else:
																		a_						:						Any 							= CrossEntropyLoss()
																		a_						:						int 							= loss_fct(logits_item.view(-1  ,      self.num_labels    )  ,      labels.view(-1    )    )
															if total_loss is None:
																		a_						:						str 							= loss
															else:
																		total_loss += loss * (ix + 1)
															total_weights += ix + 1
												a_						:						Any 							= (total_loss / total_weights,) + outputs

									return outputs


